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#include <algorithm> |
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#include <array> |
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#include <cmath> |
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#include <functional> |
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#include <limits> |
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#include <memory> |
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#include <random> |
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#include <vector> |
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#include <xnnpack.h> |
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#include <fp16/fp16.h> |
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#include "bench/utils.h" |
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#include <benchmark/benchmark.h> |
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#ifdef BENCHMARK_TENSORFLOW_LITE |
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#include "flatbuffers/include/flatbuffers/flatbuffers.h" |
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#include "tensorflow/lite/interpreter.h" |
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#include "tensorflow/lite/kernels/register.h" |
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#include "tensorflow/lite/model.h" |
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#include "tensorflow/lite/schema/schema_generated.h" |
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#include "tensorflow/lite/version.h" |
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#endif |
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static void xnnpack_elu_f16(benchmark::State& state) { |
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const size_t batch_size = state.range(0); |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-20.0f, 20.0f), std::ref(rng)); |
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auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
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std::vector<uint16_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
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std::vector<uint16_t> output(batch_size); |
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std::generate(input.begin(), input.end(), std::ref(f16rng)); |
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std::fill(output.begin(), output.end(), UINT16_C(0x7E00) ); |
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xnn_status status = xnn_initialize(nullptr ); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to initialize XNNPACK"); |
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return; |
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} |
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xnn_operator_t elu_op = nullptr; |
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status = xnn_create_elu_nc_f16( |
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1 , 1 , 1 , |
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1.0f , 0 , &elu_op); |
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if (status != xnn_status_success || elu_op == nullptr) { |
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state.SkipWithError("failed to create ELU operator"); |
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return; |
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} |
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status = xnn_reshape_elu_nc_f16(elu_op, batch_size, nullptr); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to reshape ELU operator"); |
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return; |
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} |
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status = xnn_setup_elu_nc_f16(elu_op, input.data(), output.data()); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to setup ELU operator"); |
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return; |
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} |
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for (auto _ : state) { |
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status = xnn_run_operator(elu_op, nullptr ); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to run ELU operator"); |
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return; |
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} |
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} |
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status = xnn_delete_operator(elu_op); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to delete ELU operator"); |
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return; |
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} |
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
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if (cpu_frequency != 0) { |
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state.counters["cpufreq"] = cpu_frequency; |
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} |
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state.counters["elements"] = |
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(uint16_t); |
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state.counters["bytes"] = |
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
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} |
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static void xnnpack_elu_f32(benchmark::State& state) { |
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const size_t batch_size = state.range(0); |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-20.0f, 20.0f), std::ref(rng)); |
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std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float)); |
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std::vector<float> output(batch_size); |
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std::generate(input.begin(), input.end(), std::ref(f32rng)); |
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std::fill(output.begin(), output.end(), std::nanf("")); |
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xnn_status status = xnn_initialize(nullptr ); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to initialize XNNPACK"); |
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return; |
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} |
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xnn_operator_t elu_op = nullptr; |
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status = xnn_create_elu_nc_f32( |
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1 , 1 , 1 , |
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1.0f , 0 , &elu_op); |
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if (status != xnn_status_success || elu_op == nullptr) { |
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state.SkipWithError("failed to create ELU operator"); |
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return; |
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} |
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status = xnn_reshape_elu_nc_f32(elu_op, batch_size, nullptr); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to reshape ELU operator"); |
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return; |
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} |
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status = xnn_setup_elu_nc_f32(elu_op, input.data(), output.data()); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to setup ELU operator"); |
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return; |
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} |
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for (auto _ : state) { |
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status = xnn_run_operator(elu_op, nullptr ); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to run ELU operator"); |
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return; |
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} |
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} |
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status = xnn_delete_operator(elu_op); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to delete ELU operator"); |
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return; |
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} |
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
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if (cpu_frequency != 0) { |
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state.counters["cpufreq"] = cpu_frequency; |
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} |
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state.counters["elements"] = |
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(float); |
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state.counters["bytes"] = |
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
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} |
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static void xnnpack_elu_qs8(benchmark::State& state) { |
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const size_t batch_size = state.range(0); |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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auto i8rng = std::bind( |
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
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std::ref(rng)); |
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std::vector<int8_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(int8_t)); |
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std::vector<int8_t> output(batch_size); |
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std::generate(input.begin(), input.end(), std::ref(i8rng)); |
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std::fill(output.begin(), output.end(), INT8_C(0xA5)); |
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xnn_status status = xnn_initialize(nullptr ); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to initialize XNNPACK"); |
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return; |
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} |
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xnn_operator_t elu_op = nullptr; |
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status = xnn_create_elu_nc_qs8( |
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1 , 1 , 1 , |
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1.0f , |
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0 , 1.0f , |
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0 , 1.0f , |
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std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max(), |
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0 , &elu_op); |
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if (status != xnn_status_success || elu_op == nullptr) { |
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state.SkipWithError("failed to create ELU operator"); |
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return; |
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} |
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status = xnn_reshape_elu_nc_qs8(elu_op, batch_size, nullptr); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to reshape ELU operator"); |
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return; |
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} |
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status = xnn_setup_elu_nc_qs8(elu_op, input.data(), output.data()); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to setup ELU operator"); |
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return; |
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} |
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for (auto _ : state) { |
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status = xnn_run_operator(elu_op, nullptr ); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to run ELU operator"); |
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return; |
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} |
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} |
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status = xnn_delete_operator(elu_op); |
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if (status != xnn_status_success) { |
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state.SkipWithError("failed to delete ELU operator"); |
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return; |
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} |
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
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if (cpu_frequency != 0) { |
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state.counters["cpufreq"] = cpu_frequency; |
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} |
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state.counters["elements"] = |
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(int8_t); |
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state.counters["bytes"] = |
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
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} |
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#ifdef BENCHMARK_TENSORFLOW_LITE |
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static void tflite_elu_f32(benchmark::State& state) { |
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const size_t batch_size = state.range(0); |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-20.0f, 20.0f), std::ref(rng)); |
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flatbuffers::FlatBufferBuilder builder; |
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const flatbuffers::Offset<tflite::OperatorCode> operator_code = |
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CreateOperatorCode(builder, tflite::BuiltinOperator_ELU); |
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const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
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tflite::CreateBuffer(builder, builder.CreateVector({})), |
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}}; |
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const std::array<int32_t, 1> shape{{ |
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static_cast<int32_t>(batch_size) |
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}}; |
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const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
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tflite::CreateTensor(builder, |
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builder.CreateVector<int32_t>(shape.data(), shape.size()), |
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tflite::TensorType_FLOAT32), |
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tflite::CreateTensor(builder, |
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builder.CreateVector<int32_t>(shape.data(), shape.size()), |
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tflite::TensorType_FLOAT32), |
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}}; |
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const std::array<int32_t, 1> op_inputs{{ 0 }}; |
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const std::array<int32_t, 1> op_outputs{{ 1 }}; |
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flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator( |
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builder, |
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0 , |
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
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const std::array<int32_t, 1> graph_inputs{{ 0 }}; |
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const std::array<int32_t, 1> graph_outputs{{ 1 }}; |
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const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
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builder, |
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builder.CreateVector(tensors.data(), tensors.size()), |
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builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
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builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
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builder.CreateVector(&op, 1)); |
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const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
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TFLITE_SCHEMA_VERSION, |
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builder.CreateVector(&operator_code, 1), |
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builder.CreateVector(&subgraph, 1), |
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builder.CreateString("ELU model"), |
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builder.CreateVector(buffers.data(), buffers.size())); |
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builder.Finish(model_buffer); |
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const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
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tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
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tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
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std::unique_ptr<tflite::Interpreter> interpreter; |
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if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
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state.SkipWithError("failed to create TFLite interpreter"); |
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return; |
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} |
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interpreter->SetNumThreads(1); |
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if (interpreter->AllocateTensors() != kTfLiteOk) { |
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state.SkipWithError("failed to allocate tensors"); |
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return; |
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} |
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std::generate( |
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interpreter->typed_tensor<float>(0), |
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interpreter->typed_tensor<float>(0) + batch_size, |
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std::ref(f32rng)); |
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for (auto _ : state) { |
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if (interpreter->Invoke() != kTfLiteOk) { |
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state.SkipWithError("failed to invoke TFLite interpreter"); |
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return; |
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} |
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} |
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
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if (cpu_frequency != 0) { |
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state.counters["cpufreq"] = cpu_frequency; |
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} |
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state.counters["elements"] = |
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(float); |
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state.counters["bytes"] = |
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
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interpreter.reset(); |
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} |
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static void tflite_elu_qs8(benchmark::State& state) { |
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const size_t batch_size = state.range(0); |
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std::random_device random_device; |
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auto rng = std::mt19937(random_device()); |
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auto i8rng = std::bind( |
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
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std::ref(rng)); |
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flatbuffers::FlatBufferBuilder builder; |
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const flatbuffers::Offset<tflite::OperatorCode> operator_code = |
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CreateOperatorCode(builder, tflite::BuiltinOperator_ELU); |
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const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
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tflite::CreateBuffer(builder, builder.CreateVector({})), |
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}}; |
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const std::array<int32_t, 1> shape{{ |
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static_cast<int32_t>(batch_size) |
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}}; |
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const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
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tflite::CreateTensor(builder, |
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builder.CreateVector<int32_t>(shape.data(), shape.size()), |
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tflite::TensorType_INT8, 0 , 0 , |
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tflite::CreateQuantizationParameters(builder, |
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0 , 0 , |
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builder.CreateVector<float>({1.0f }), |
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builder.CreateVector<int64_t>({1 }))), |
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tflite::CreateTensor(builder, |
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builder.CreateVector<int32_t>(shape.data(), shape.size()), |
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tflite::TensorType_INT8, 0 , 0 , |
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tflite::CreateQuantizationParameters(builder, |
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0 , 0 , |
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builder.CreateVector<float>({1.0f }), |
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builder.CreateVector<int64_t>({1 }))), |
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}}; |
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const std::array<int32_t, 1> op_inputs{{ 0 }}; |
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const std::array<int32_t, 1> op_outputs{{ 1 }}; |
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flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator( |
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builder, |
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0 , |
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
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const std::array<int32_t, 1> graph_inputs{{ 0 }}; |
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const std::array<int32_t, 1> graph_outputs{{ 1 }}; |
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const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
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builder, |
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builder.CreateVector(tensors.data(), tensors.size()), |
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builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
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builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
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builder.CreateVector(&op, 1)); |
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const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
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TFLITE_SCHEMA_VERSION, |
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builder.CreateVector(&operator_code, 1), |
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builder.CreateVector(&subgraph, 1), |
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builder.CreateString("ELU model"), |
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builder.CreateVector(buffers.data(), buffers.size())); |
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builder.Finish(model_buffer); |
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const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
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tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
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tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
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std::unique_ptr<tflite::Interpreter> interpreter; |
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if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
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state.SkipWithError("failed to create TFLite interpreter"); |
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return; |
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} |
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interpreter->SetNumThreads(1); |
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if (interpreter->AllocateTensors() != kTfLiteOk) { |
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state.SkipWithError("failed to allocate tensors"); |
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return; |
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} |
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std::generate( |
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interpreter->typed_tensor<int8_t>(0), |
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interpreter->typed_tensor<int8_t>(0) + batch_size, |
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std::ref(i8rng)); |
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for (auto _ : state) { |
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if (interpreter->Invoke() != kTfLiteOk) { |
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state.SkipWithError("failed to invoke TFLite interpreter"); |
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return; |
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} |
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} |
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const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
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if (cpu_frequency != 0) { |
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state.counters["cpufreq"] = cpu_frequency; |
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} |
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state.counters["elements"] = |
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benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
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const size_t bytes_per_iteration = 2 * batch_size * sizeof(int8_t); |
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state.counters["bytes"] = |
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benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
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interpreter.reset(); |
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} |
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#endif |
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BENCHMARK(xnnpack_elu_f16) |
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->Apply(benchmark::utils::UnaryElementwiseParameters<uint16_t, uint16_t>) |
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->UseRealTime(); |
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BENCHMARK(xnnpack_elu_f32) |
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->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>) |
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->UseRealTime(); |
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BENCHMARK(xnnpack_elu_qs8) |
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->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>) |
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->UseRealTime(); |
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#ifdef BENCHMARK_TENSORFLOW_LITE |
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BENCHMARK(tflite_elu_f32) |
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->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>) |
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->UseRealTime(); |
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BENCHMARK(tflite_elu_qs8) |
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->Apply(benchmark::utils::UnaryElementwiseParameters<int8_t, int8_t>) |
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->UseRealTime(); |
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#endif |
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|
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#ifndef XNNPACK_BENCHMARK_NO_MAIN |
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BENCHMARK_MAIN(); |
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#endif |
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